Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation

Abstract

Spamming and phishing are two types of emailing that are annoying and unwanted, differing by the potential threat and impact to the user. Automated classification of these categories can increase the users’ awareness as well as to be used for incident investigation prioritization or automated fact gathering. However, currently there are no scientific papers focusing on email classification concerning these two categories of spam and phishing emails. Therefore this paper presents a solution, based on email message body text automated classification into spam and phishing emails. We apply the proposed solution for email classification, written in three languages: English, Russian, and Lithuanian. As most public email datasets almost exclusively collect English emails, we investigate the suitability of automated dataset translation to adapt it to email classification, written in other languages. Experiments on public dataset usage limitations for a specific organization are executed in this paper to evaluate the need of dataset updates for more accurate classification results.

Machine Learning and Deep Learning for Phishing Email Classification using One-Hot Encoding

“…They achieve an accuracy of 99.1% using Random Forest and 98.4% using J48. Rastenis et al (2021) presents a solution based on the email message body using text classification, classifying emails into spam and phishing emails. Fang et al (2019) presented a recurrent CNN model with multilevel vectors and proposed a new phishing email detection model, Themis.…”

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

“…The dataset with 4601 instances (1813 spam and 2788 non-spam messages) from the UCI Machine-Learning Repository was applied for analysis. Rastenis et al [24] proposed an automated spam and phishing e-mail classification solution, which is based on e-mail message body text automated classification. It also solves the problem of correct classification of e-mails written in different languages.…”

“…Text preprocessing plays a crucial role in spam filtering [24,37]. For any spam detection model to be effective, the content of the e-mails should be normalized and represented as feature vectors.…”

Robust Text Classifier for Classification of Spam E-Mail Documents with Feature Selection Technique

“…They used the neural network technique along with a heterogeneous graph to connect the nodes for concluding about the opinion spam. The main theme of this paper [17] is to classify spam and phishing e-mails. They used the body structure of the email and applies deep-learning and FSTs to identify the emails into different categories.…”